Solved – After trying various optimzers, model simplification running more iterations, when fitting GLMMs, R still produces warning messages

by

I am trying to fit GLMM's to my data using the glmer function available in R's lme4 package. The data is available at: https://onedrive.live.com/redir?resid=1B727FC7180E87DF%21118

I keep getting warning messages. Can anybody help me get rid of them.

I am re-posting this from StackOverflow after someone's kind suggestion. That person also suggested that the main of the issue may be low number of virus positive samples n=12. Which I suspected. But I am also wandering if linear separation could be an issue, as all the virus positives occur in the low food group. Can these problems be resolved using GLMMs or should I think of other statistical tests?

Tried fitting the model:

Food_Treatment.glmer <- glmer(Virus_DNA~Food*Treatment+(1|Set),                               family=binomial,data=data,method = "ML")

to get the warning messages

Warning messages: 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :       Model failed to converge with max|grad| = 0.001101 (tol = 0.001, component 3) 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :       Model failed to converge: degenerate Hessian with 4 negative eigenvalues

After running code with more iterations of the model, I still get the same warning messages:

Food_Treatment.glmer <- glmer(Virus_DNA~Food*Treatment+(1|Set),data=data,                              family=binomial,control=glmerControl(optCtrl=list(maxfun=1e9)))

I then looked on-line and that people had similar problems and tried the optmizer bobyqa:

Food_Treatment.glmer <- glmer(Virus_DNA~Food*Treatment(1|Set),data=data,family=binomial,                           control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=1e9)))

I then got the very similar warning messages:

Warning messages: 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :       Model failed to converge with max|grad| = 0.00393532 (tol = 0.001, component 2) 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :       Model failed to converge: degenerate Hessian with 2 negative eigenvalues

I then thought of simplifying the model and tried no interactions between explanatory variables, with the code:

Food.Plus.Treatment.glmer<-glmer(Virus_DNA~Food+Treatment(1|Set),family=binomial,                                  data=data)

and

Food.Plus.Treatment.glmer<-glmer(Virus_DNA~Food+Treatment(1|Set),family=binomial,                                  data=data,control=glmerControl(optCtrl=list(maxfun=1e9)))

Only to get the warning messages :

Warning messages: 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :       Model failed to converge with max|grad| = 0.00248016 (tol = 0.001, component 2) 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :        Model failed to converge: degenerate Hessian with 1 negative eigenvalues

So I tried this simplified model with the optimizers bobyqa and Nelder_Mead, as well as the optimzers nlminb and L-BFGS-B from the package optimx.

All but the bobyqa optimizers produce variations on the 2 warning messages. The bobyqa optimizer produces the 1 warning message:

Warning message: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :      Model failed to converge with max|grad| = 0.00139574 (tol = 0.001, component 2)

P.S. This is my first post on here I hope I have provided enough information without being verbose and it is correctly formatted.

Contents hide
1 Best Answer
2 Similar Posts:

Best Answer

Variable Set has only 6 categories. My guess is that this is not enough information to appropriately estimate the variance of the random effects. Most books on multilevel modeling suggest sample sizes of $geq 15$ for level two units. You might want to include Set as a fixed effect and estimate a simpler glm() model.

Here are a few references:

Similar Posts:

Rate this post

Leave a Comment